Implement knowledge mining and information extraction solutions →mediumMultiple Choice

AI-102 Practice Question: Implement knowledge mining and information extraction solutions

You are building an Azure AI Search solution that enriches documents by detecting the language of each document and then routing content to language-specific analyzers. You add a LanguageDetectionSkill to the skillset and want the detected language code to be available to downstream skills and to be stored in the index. The detected language must be mapped to a field named 'languageCode' in the index. What should you do?

⚠ Common exam trap

The trap here is assuming that enabling a skill and adding a matching index field is enough, when the indexer still needs an explicit output field mapping to persist the enriched value.

Answer choices

Why each option matters

Answer the question above first, then reveal the full breakdown to understand why each option is right or wrong.

Correct answer & explanation

✓

Add an outputFieldMapping in the indexer that maps the '/document/languageCode' enrichment node to the 'languageCode' index field.

The LanguageDetectionSkill outputs a language code under the enriched document, but that value only reaches the index if the indexer maps it. Output field mappings declare which enrichment nodes become index field values. Configuring analyzers or field attributes changes how data is stored or queried, not whether it is stored. A custom skill is unnecessary because the built-in skill already emits the required value.

Answer analysis

Option-by-option breakdown

For each option: why learners choose it and why it is or isn't the right answer here.

✗
Configure the LanguageDetectionSkill with a 'defaultLanguageCode' parameter and set the index field 'languageCode' to use the 'fr.lucene' analyzer.
Why it's wrong here
Setting defaultLanguageCode only provides a fallback when detection fails; it does not map the skill output to an index field. The analyzer choice affects query-time tokenization, not enrichment output mapping. This option confuses skill configuration with index projection, so the detected language code would never be written to the 'languageCode' field.
✗
Create a custom skill that calls the Azure AI Language service and writes the detected language directly into the index using the Azure AI Search REST API.
Why it's wrong here
A custom skill runs within the enrichment pipeline and returns enriched content; it cannot directly write to the index because the indexer controls indexing. Using the REST API from a skill introduces unnecessary complexity and bypasses the pipeline. The built-in LanguageDetectionSkill already produces the needed value, so a custom skill is not required for this scenario.
✗
Set the 'languageCode' field in the index to be retrievable and filterable, and rely on the indexer to automatically populate it from the skill output.
Why it's wrong here
Making a field retrievable or filterable defines its capabilities but does not populate it. The indexer does not automatically map arbitrary skill outputs to index fields; an explicit output field mapping is required. Without that mapping, the field remains empty even though the skill produced a language code during enrichment.
✓
Add an outputFieldMapping in the indexer that maps the '/document/languageCode' enrichment node to the 'languageCode' index field.
Why this is correct
Output field mappings in the indexer explicitly connect enriched document nodes to index fields. The LanguageDetectionSkill emits a 'languageCode' value under /document, and an outputFieldMapping with sourceFieldName '/document/languageCode' and targetFieldName 'languageCode' persists it. This is the supported mechanism for projecting skill output into the search index.

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AI-102 Practice Question: Implement knowledge mining and information extraction solutions

Why each option matters

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